Abstract
Keywords
Background
Osteoporosis is a systemic musculoskeletal disorder characterised by reduced bone mineral density (BMD) and the deterioration of bone tissue microstructure. 1 It is notable for its high prevalence, affecting approximately one-third of females and one-fifth of males aged ≥50 years globally.2–4 An epidemiological study in China revealed an osteoporosis prevalence of 5.0% among males and 20.6% among females aged ≥40 years. 5 As the population continues to age, the prevalence of osteoporosis is expected to rise.6,7 Moreover, osteoporosis imposes significant financial and societal burdens, with annual costs estimated at $17.9 billion in the United States (US) and £4 billion in the United Kingdom. 8 The disorder is also associated with high rates of disability and mortality, particularly due to serious complications such as osteoporotic fractures, including hip fractures, which significantly affect the health and quality of life of elderly individuals.9–11 Therefore, early prevention of osteoporosis is a crucial focus of ongoing research.
Investigating both risk and protective factors for diseases is crucial for developing effective early prevention strategies. Osteoporosis, from a pathological viewpoint, encompasses a complex interplay of bone metabolism, hormonal regulation, and immune modulation.1,12 The aetiology of osteoporosis is associated with a range of factors.1,12,13 Evidence indicated that key risk factors include age, sex, menopausal status, race, physical activity levels, and nutritional intake.13,14 Furthermore, numerous studies have suggested that using these risk factors to identify and predict osteoporosis risk can be an effective strategy for early detection and prevention.15,16 Therefore, there is a growing emphasis on investigating the factors associated with osteoporosis in current research.
The uric acid (UA)-to-high-density lipoprotein cholesterol (HDL-C) ratio (UHR) is a novel marker indicative of metabolic and inflammatory conditions. Previous research has linked UHR to an increased risk of various illnesses and demonstrated its potential as a predictor for several clinical outcomes.17–20 Xie et al. found that a high UHR was independently associated with the risk and severity of non-alcoholic fatty liver disease in the American population, with correlations varying by sex. 20 Zhu et al. posited that elevated UHR could serve as a cost-effective and reliable predictor for the onset of non-alcoholic fatty liver disease in non-obese Chinese individuals with normal blood lipid levels. 21 Furthermore, Yang et al. identified an association between elevated UHR and an increased likelihood of adverse cardiovascular events in individuals with chronic total occlusion of the coronary arteries. 22 Yang et al. also demonstrated that the combined presence of high UA and low HDL-C had a synergistic effect on risk stratification for acute myocardial infarction. 23 These findings suggest that UHR might serve as a valuable and independent prognostic marker, complementing traditional risk factors. However, the relationship between UHR and osteoporosis risk remains to be definitively established. Further research is needed to explore this association and potentially identify early predictive markers for osteoporosis.
This study aims to examine the correlation between UHR and BMD, as well as the risk of osteoporosis, using data from the National Health and Nutrition Examination Survey (NHANES). Our hypothesis posited that elevated UHR would be positively correlated with higher BMD and a reduced risk of osteoporosis.
Methods
Study design and population
This cross-sectional study used data from the NHANES 2017–March 2020 cycle. NHANES is a biennial survey conducted by the National Centre for Health Statistics (NCHS) to evaluate the health and nutritional status of the US population. All participants provided written informed consent, and the study was approved by the Institutional Review Board of the NCHS. Participants were included if they met the following criteria: (i) aged ≥50 years, (ii) had complete BMD data, and (iii) had complete UHR data. Individuals with missing data on covariates were excluded from the analysis. Additional details are available on the NHANES website. 24
BMD measurement and osteoporosis
BMD was measured using dual-energy X-ray absorptiometry, with femoral neck BMD (FN-BMD) selected as the outcome variable due to its established association with hip fracture risk. 25 Osteoporosis was defined based on BMD results and the World Health Organisation criteria, where a T-score of ≤ −2.5 for FN-BMD is indicative of osteoporosis. Specific values for FN-BMD were derived from previous studies (BMD: ≤0.588 g/cm2 for males and ≤0.560 g/cm2 for females).26–28 Detailed information on the BMD measurement methodology is available on the NHANES website. 29
UHR assessment
In this study, UHR was considered the exposure variable. According to a previous study, 18 UHR is calculated using the formula: UHR (%) = UA (mg/dL)/HDL-C (mg/dL). Detailed procedures for measuring UA and HDL-C are available on the NHANES website. 30
Covariates
To account for potential confounding variables that could influence both the outcome and exposure variables, this study included multiple covariates in the analyses. The covariates considered were as follows: age; sex/menopausal status (post-menopausal women, pre-menopausal women, or men); race (non-Hispanic White, non-Hispanic Black, Mexican American, or other races); education level (below high school, high school or equivalent, or above high school); body mass index (BMI) categories (normal: BMI <25 kg/m2; overweight: 25≤ BMI <30 kg/m2; or obese: BMI ≥30 kg/m2); smoking status (never, former, or current); drinking status (never, former, or current); physical activity; vitamin D intake; calcium intake; milk product consumption (never, rarely, sometimes, often, or varied); history of diabetes (yes or no); history of arthritis (yes or no); history of cancer (yes or no); history of fractures (yes or no); history of glucocorticoid use (yes or no); alanine aminotransferase (U/L); aspartate aminotransferase (U/L); alkaline phosphatase (IU/L); blood urea nitrogen (BUN) (mmol/L); serum creatinine (mg/dL); serum calcium (mg/dL); serum total cholesterol (mg/dL); serum triglycerides (TGs) (mg/dL); serum HDL-C; and serum UA (mg/dL). Detailed information on the assessment of these covariates is available on the NHANES website. 24
Statistical analysis
Baseline characteristics are reported as the mean ± standard deviation for continuous variables and as n (%) for categorical variables. Differences in baseline characteristics among groups stratified by UHR were assessed using one-way analysis of variance for continuous variables and the chi-square test for categorical variables. Linear regression models were employed to investigate the association between UHR and FN-BMD, while logistic regression models were employed to examine the association between UHR and osteoporosis risk. The nonlinear relationship between UHR and FN-BMD was explored using generalised additive models. Furthermore, subgroup analyses and interaction tests were performed to evaluate the relationship between UHR and BMD, as well as the risk of osteoporosis, across different populations categorised by age, sex/menopausal status, race/ethnicity, and BMI. UHR values were also converted from a continuous variable to a categorical variable (quartile 1 to quartile 4) (Q1-Q4) to examine their association with FN-BMD and osteoporosis risk. Data analysis was conducted using R software version 4.2.1 (https://cran.r-project.org/) and EmpowerStats version 2.0 (https://www.empowerstats.com). A significance level of p < .05 was used to determine statistical significance.
Results
Selection of the study population
This study initially included 15,560 participants from the NHANES 2017–March 2020 cycle. Participants aged <50 years and those with missing data on BMD, UHR, or relevant covariates were excluded. Ultimately, 2963 participants aged ≥50 years were retained for the final analysis. A detailed flowchart illustrating the inclusion and exclusion criteria is presented in Figure 1. Flowchart of the study population selection process. BMD: bone mineral density; NHANES: national health and nutrition examination survey; UHR, uric acid to high-density lipoprotein cholesterol ratio.
Baseline characteristics
Baseline characteristics of study population.
Normal BMI <25 kg/m2, overweight 25 ≤ BMI <30 kg/m2, obesity BMI ≥30 kg/m2.
BMD: bone mineral density; BMI: body mass index, HDL-C: high-density lipoprotein cholesterol; UA: uric acid; UHR: uric acid to high-density lipoprotein cholesterol ratio.
Covariate screening
Covariates with a p-value of <.1 in the univariate linear regression analysis (with FN-BMD as the outcome) were selected for further analysis. Finally, all covariates except for education level, milk product consumption, history of arthritis, BUN, and TGs were included in the subsequent analysis. For detailed participant information, please refer to Supplemental Table S1. Multicollinearity between UHR and the included covariates was evaluated using variance inflation factor (VIF) values, with a VIF of >5 indicating multicollinearity. The results indicated no multicollinearity between UHR and the included covariates. Additional details are provided in Supplemental Table S2.
Association between UHR and BMD
Association between UHR and FN-BMD.
Model 1: no covariates were adjusted; Model 2: age, sex/menopausal status, race/ethnicity, and BMI were adjusted; Model 3: all covariates were adjusted.
BMD: bone mineral density; BMI: body mass index; FN: femoral neck; UHR: uric acid to high-density lipoprotein cholesterol ratio.
aAge was not adjusted.
bSex/menopausal status was not adjusted.
cRace/ethnicity was not adjusted.
dBMI was not adjusted.

Non-linear relationship between UHR and FN-BMD. (a) Model 1; (b) Model 2; (c) Model 3. Model 1: no covariates were adjusted; Model 2: age, sex/menopausal status, race/ethnicity, and BMI were adjusted; Model 3: all covariates were adjusted. Red line represents the fitting curve; blue line represents the 95% CI. BMD: bone mineral density; BMI: body mass index; CI: confidence interval; FN: femoral neck; UHR: uric acid to high-density lipoprotein cholesterol ratio.
Association between UHR and osteoporosis risk
Association between UHR and the risk of osteoporosis.
Model 1: no covariates were adjusted; Model 2: age, sex/menopausal status, race/ethnicity, and BMI were adjusted; Model 3: all covariates were adjusted.
BMI: body mass index; CI: confidence interval; OR: odds ratio; UHR: uric acid to high-density lipoprotein cholesterol ratio.
aAge was not adjusted.
bSex/menopausal status was not adjusted.
cRace/ethnicity was not adjusted.
dBMI was not adjusted.
Sensitivity analysis
Association between UHR (Q1 to Q4) and FN-BMD (and the risk of osteoporosis).
Model 1: no covariates were adjusted; Model 2: age, sex/menopausal status, race/ethnicity, and BMI were adjusted; Model 3: all covariates were adjusted.
BMD: bone mineral density; CI: confidence interval; FN: femoral neck; OR: odds ratio; UHR: uric acid to high-density lipoprotein cholesterol ratio.
Discussion
The increasing incidence of osteoporosis among middle-aged and elderly individuals, which significantly diminishes the quality of life for older adults, has established osteoporosis as a global public health concern.1,12 In this cross-sectional study, UHR was positively associated with FN-BMD and negatively associated with osteoporosis risk in individuals aged ≥50 years. Subgroup analyses further revealed that the positive association between UHR and FN-BMD was observed in individuals aged ≥65 years, but not in those aged 50–64 years, with interaction analysis confirming significant age-related differences after adjusting for all covariates.
This study identified a positive association between UHR and BMD, with a higher UHR associated with a reduced risk of osteoporosis. Previous research has reported a positive association between UA levels and BMD.31–34 For instance, Jin et al. reported a positive association between UA and BMD in males aged ≥50 years, 33 while Xu et al. reported that serum UA levels were positively associated with BMD among patients with osteoporosis. 34 Yao et al. demonstrated a positive correlation between UA and lumbar spine BMD in elderly individuals. 35 However, the relationship between HDL-C and BMD remains contentious, as several studies have indicated a negative correlation between HDL-C and BMD.36,37 Tang et al. observed a negative association between HDL-C and BMD in individuals aged ≥20 years. 36 Wang et al. identified a similar negative correlation in male adolescents aged 12 to 19 years, particularly among non-black and non-Mexican populations. 37 In contrast, Xie et al. reported a positive correlation between HDL-C and lumbar spine BMD in individuals aged 20 to 59 years. 38 The aforementioned findings might contribute to the observed positive association between UHR and BMD. However, further research with larger sample sizes is necessary to elucidate the relationship between UHR and BMD.
The current study identified a positive correlation between UHR and FN-BMD in individuals aged ≥65 years, but not in those aged 50 to 64 years. This discrepancy might be attributed to several factors. The prevalence of osteoporosis increases significantly with age,1,7,12 and age itself is a significant independent factor influencing BMD.1,6,12 Additionally, the relatively small sample size of individuals with osteoporosis in the 50–64 age group (N = 56) in this study might have limited the ability to detect a similar association. Therefore, further research is needed to better understand these age-related differences.
The main findings of this study might have implications for future research. The novel inflammation and metabolic indicator, UHR—comprising UA and HDL-C—has been associated with various clinical conditions such as hypertension, diabetes, metabolic syndrome, and non-alcoholic fatty liver disease.18,39–41 To the best of our knowledge, this is the first study to investigate the association between UHR and BMD, as well as the risk of osteoporosis. Importantly, it was found that higher UHR values were associated with a reduced osteoporosis risk, suggesting that UHR could serve as a novel indicator for identifying and predicting osteoporosis risk. Furthermore, the study revealed that the correlation between UHR and FN-BMD was significant only in individuals aged ≥65 years, not in those aged 50 to 64 years. The interaction analysis further highlighted significant age-related differences after adjusting for covariates. Although the reasons for these age disparities remain unclear, it is imperative to consider age differences when using UHR as a marker for osteoporosis risk prediction.
This study has several limitations that should be acknowledged. First, due to its cross-sectional design, the study does not allow for causal interpretations between UHR and osteoporosis. Second, some covariates were collected through self-reported questionnaires, which might introduce recall bias. Third, the sample size of participants with osteoporosis was relatively small. Further studies with large sample sizes are needed to better understand the relationship between UHR and osteoporosis risk. Lastly, since all participants were from the US, the findings might not apply to populations in other countries.
Conclusion
Clinicians should be vigilant about the potential risk of osteoporosis in individuals with low UHR, as UHR could serve as a risk indicator for this condition.
Supplemental Material
Supplemental Material - Uric acid-to-high-density lipoprotein cholesterol ratio and osteoporosis: evidence from the national health and nutrition examination survey
Supplemental Material for Uric acid-to-high-density lipoprotein cholesterol ratio and osteoporosis: evidence from the national health and nutrition examination survey by Zeyu Liu, Yuchen Tang, Ying Sun, Miao Lei, Minghuang Cheng, Xiaohan Pan, Zhenming Hu and Jie Hao in Journal of Orthopaedic Surgery
Footnotes
Acknowledgements
We thank Bullet Edits Limited for the linguistic editing and proofreading of the manuscript. The authors extend their gratitude to the participants for their time and effort during the data collection phase of the NHANES project.
Author contributions
ZL and YT have contributed equally to this work. ZL: Conceptualisation, Formal analysis, Data Curation, Writing-Original Draft, and Writing-Review & Editing; YT: Conceptualisation, Methodology, Validation, Writing-Original Draft, and Writing-Review & Editing; YS: Software, Data Curation, Visualisation, and Writing-Review & Editing; ML: Validation and Writing-Review & Editing; MC: Software and Writing-Review & Editing; XP: Investigation and Writing-Review & Editing; ZH: Conceptualisation, Writing-Review & Editing and Supervision; JH: Conceptualisation, Methodology, Writing-Review & Editing, and Supervision.
Declaration of conflicting interests
The author(s) declare that there are no potential conflicts of interest related to the research, authorship, and/or publication of this article.
Funding
The author(s) received no financial support for the research, authorship, and/or publication of this article.
Ethical statement
Data availability statement
Supplemental Material
Supplemental material for this article is available online.
References
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
